Medical image noise reduction method, system, terminal, and storage medium

ABSTRACT

A medical image noise reduction method, system, terminal and storage medium are disclosed. The method includes: obtaining a standard-dose PET image and a. constant-value image; inputting the standard-dose PET image and the constant-value image into a decay function to obtain a corresponding low-dose noisy PET image and a noisy constant-value image, respectively; assembling the low-dose noisy PET image and the noisy constant-value image in a width dimension or a height dimension, and then inputting into a trained conjugate generative adversarial network, and outputting a denoised PET image and constant-value image output by the conjugate generative adversarial network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending International PatentApplication Number PCT/CN2020/135431, filed on Dec. 10, 2020, thedisclosure of which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present application relates to the technical field of medical imageprocessing, and more particularly relates to a medical image noisereduction method, system, terminal and storage medium.

BACKGROUND

The problem of image denoising has been around for a long time. Afterthe application of deep learning in many fields has achieved resultsthat surpass traditional methods, deep learning has also produced manyapplications in image denoising, and has made great progress. TheGenerative adversarial network (GAN) in deep learning, due to itsingenious structure and excellent performance, is also increasingly usedin the denoising of medical images.

Taking a PET (Positron Emission Tomography) image as an example, PET isa type of ECT (Emission Computed Tomography). The basic operatingprinciple is as follows. First, a radioactive tracer is injected intothe circulatory system of the human body, and then a detector is used tocollect and annihilate a photon pair, so that activity intensities ofdifferent tissues in the human body can be distinguished according tothe brightness difference caused by the concentrations of differentcomponents of the radioactive tracer in different tissues. It canprovide three-dimensional functional, metabolic and receptor imagingimages non-invasively—multi-modality imaging. However, the cumulativeeffect of large PET radiation doses greatly increases the possibility ofvarious diseases, which in turn affects the physiological functions ofthe human body, destroys human tissues and organs, and even endangersthe life safety of patients. The rational application of low-dose PETimaging technology needs to meet the clinical diagnostic requirements ofPET images, while reducing the impact of radiation doses on patients asmuch as possible. Therefore, research and development of PET imagingwith higher imaging quality under low-dose conditions has importantscientific significance and broad application prospects for the currentmedical field.

In 2018, Y. Wang et al. published an article “3D conditional generativeadversarial networks for high-quality PET image estimation at low dose”in Elsevier's NeuroImage journal, applying conditional generativeadversarial networks (conditional GAN) to estimation of high-quality PETimages from low-dose PET images of the brain. The technique processesimages in pairs, namely a low-dose PET image (a low-quality image withnoise) and a high-dose PET image (a high-quality image). The low-dosePET image serves as input to the generator in the generative adversarialnetwork and as the condition of the discriminator, while the high-dosePET images serves as a “label” in supervised learning to be input to andtrain the discriminator.

In 2019, YangLei et al. published an article “Whole-body PET estimationfrom low count statistics using cycle-consistent generative adversarialnetworks” in IOP's Phys. Med Biol journal, applying the cycle generativeadversarial network (CycleGAN) to estimate a high-quality PET image froma whole-body low-dose PET image. The cycle generative adversarialnetwork mainly includes two generative adversarial networks, onegenerative adversarial network Obtains the denoised PET image from thelow-dose PET image, and the other generative adversarial network in theopposite direction takes the denoised PET image obtained by the firstgenerative adversarial network as input, and obtains a noisy PET imageas close as possible to the original low-dose PET image. In addition tothe loss function of the original generative adversarial network, thecycle generative adversarial network increases the loss function betweenthe original low-dose PET image and the generated noisy PET image, andthe loss function between the original high-quality PET image and thegenerated denoised PET image. These two loss functions are also calledcycle-consistent loss functions) to ensure the cycle-consistency acrossthe entire network. The original cycle generative adversarial network isused to deal with unpaired images, i.e. there is no one-to-onecorrespondence between low-dose PET images and high-quality PET images.

However, most of the above-mentioned solutions to the PET imagedenoising problem using generative adversarial networks simplytransplant the structures proposed in computer vision problems. In fact,the goal of the original generative adversarial network is mainly imagestyle transfer, or converting semantic segmentation or instancesegmentation reticle into real images, which cannot be well applied toimage denoising. In addition, since the Generative adversarial networkwas originally “unsupervised” or “weakly supervised”, in order toachieve Nash equilibrium, repeated “trial and error” of the model wasrequired, resulting in unstable model training and difficulty inconvergence. Furthermore, existing low-dose PET image denoising, modelsbased on generative adversarial networks has poor generalizationperformance.

SUMMARY

The present application provides a medical image noise reduction method,system, terminal and storage medium, aiming to solve one of theabove-mentioned technical problems in the prior art at least to acertain extent.

In order to solve the above problems, the application provides thefollowing technical solutions.

A medical image noise reduction method, comprising:

obtaining a standard-dose PET image and a constant-value image;

inputting the standard-dose PET image and the constant-value image intoa decay function to obtain the respective low-dose noisy PET image andnoisy constant-value image;

assembling the low-dose noisy PET image and the noisy constant-valueimage in the width dimension or the height dimension, and then inputtinginto a trained conjugate generative adversarial network, and outputtingthe denoised PET image and constant-value image output through theconjugate generative adversarial network.

The technical solutions adopted in the embodiments of the presentapplication further include: the conjugate generative adversarialnetwork includes a generator and a discriminator;

the generator includes a reflective padding layer, a convolution layer,an instance normalization layer, a nonlinear layer, a residual module,an upsampling layer, and a nonlinear layer;

the discriminator is a convolutional neural network classifier, and thediscriminator includes a convolutional layer, an instance normalizationlayer, and a nonlinear layer.

The technical solutions adopted in the embodiments of the presentapplication further include: the generator includes two parts, featureextraction and image reconstruction;

In the feature extraction part, firstly, the input low-dose noisy PETimage and noisy constant-value image are processed using the paddinglayer, the convolutional layer, the instance normalization layer and thenonlinear layer; secondly, use four groups of feature extraction modulesto perform feature extraction on the low-dose noisy PET image and thenoisy constant-value image; then, process the extracted features throughthree residual modules;

In the image reconstruction part, firstly, the PET image and theconstant-value image are gradually reconstructed according to theextracted features through four upsampling modules; then thereconstructed PET image the constant-value image are processed using thepadding layer, the convolution layer and the nonlinear layer, and outputdenoised PET image and constant-value image.

The technical solutions adopted in the embodiments of the presentapplication further include the following. The feature extraction moduleincludes a convolution layer, an instance normalization layer and anonlinear layer, where the convolution layer step size of each featureextraction module is 2; with the gradual increase of the extractionmodules, the size of each side of the output feature map of theconvolutional layer becomes half of that of the previous featureextraction module, and the number of the feature maps is twice that ofthe previous feature extraction module.

The technical solutions adopted in the embodiments of the presentapplication further include: assembling the PET image and theconstant-value image generated by the generator with the low-dose noisyPET image and the noisy constant-value image in the channel dimensionrespectively, and inputting them into the discriminator; then, thediscriminator performs three sets of convolution, instance normalizationand nonlinear operations, and finally uses a convolution layer to outputthe classification results of the PET image and the constant-value imagegenerated by the generator.

The technical solutions adopted in the embodiments of the presentapplication further include: the loss function of the conjugategenerative adversarial network includes a first loss function used whentraining the discriminator and a second loss function used when trainingthe generator, where the first loss function is represented by the meansquare error as:

L _(D)=

_(β˜P) _(β) [(D(β, α)−b)²]+

_(α˜P) _(α) [(D(G(α), α)−α)²]

in the above formula, D represents the discriminator network, Grepresents the generator network,

represents the expectation, α represents the input low-dose noisy PETimage, β represents the real standard-dose PET image, a represents 0,and b represents 1;

The second loss function is expressed as the following using one-normloss function and mean square error loss function:

L _(l1)=

_(α˜P) _(α) ∥G(α)−γ∥₁

L _(gan)=

_(α˜P) _(α) [(D(G(α), α)−b)²]

In the above formula, ∥*∥₁ represents one norm, and γ represents theimage after assembling the real standard-dose PET image β and theconstant-value image.

The technical solutions adopted in the embodiments of the presentapplication further include: the loss function of the conjugategenerative adversarial network further includes a feature matching lossfunction:

$L_{feat} = {{\mathbb{E}}_{({\alpha,\beta})}{\sum\limits_{i = 1}^{T}{\frac{1}{N_{i}}{{{D^{(i)}\left( {\beta,\alpha} \right)} - {D^{(i)}\left( {{G(\alpha)},\alpha} \right)}}}_{1}}}}$

In the above formula, D^(i) represents the i layer of the discriminator,N_(i) represents the number of elements in each layer, and T representsthe total number of layers of the discriminator.

Another technical solution adopted by the embodiments of the presentapplication is a medical image noise reduction system, comprising:

an original image acquisition module used to obtain a standard-dose PETimage and a constant-value image;

an image attenuation module used to input the standard-dose PET imageand the constant-value image into a decay function to obtain therespective low-dose noisy. PET image and noisy constant-value image; and

an image denoising module used to assemble the low-dose noisy PET imageand the noisy constant-value image in the width dimension or the heightdimension, and then inputting into a trained conjugate generativeadversarial network, and outputting the denoised PET image andconstant-value image output through the conjugate generative adversarialnetwork.

A Another technical solution adopted by the embodiments of the presentapplication is a terminal, the terminal includes a processor and amemory coupled to the processor, wherein,

the memory stores program instructions for implementing the medicalimage noise reduction method;

the processor is configured to execute the program instructions storedin the memory to control medical image noise reduction.

Another technical solution adopted by the embodiments of the presentapplication is a storage medium storing program instructions executableby a processor, where the program instructions are used to execute themedical image noise reduction method.

Compared with the prior art, the beneficial effects of the embodimentsof the present application are as follows. The medical image noisereduction method, system, terminal and storage medium of the embodimentsof the present application perform medical image noise reduction byconstructing a conjugate generative adversarial network, and the networkstructure adopts the conjugation mechanism of image conversion, whichstrengthens the constraints on the generative adversarial network, whichcan strengthen the supervision of model training, highlight the trainingobjectives of the model, speed up the convergence speed of the model,and enable the model to learn more essential features, enhance thegeneralization performance of the model, and improve the stability ofthe model, making the training of the medical image noise reductionmodel easier. While improving the peak signal-to-noise ratio andstructural similarity of the image, the image processing capability isenhanced, the quality of low-dose PET imaging is improved, the varianceis reduced, and a more stable and reliable noise reduction effect isobtained. Furthermore, the one-norm loss function and feature matchingloss function of the generated image and the real image are added, whicheffectively improves the quality of the generated image and can bettersupervise the network to approximate the real image.

BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS

FIG 1 is a flowchart of a medical image noise reduction method accordingto an embodiment of the present application.

FIG. 2 is a schematic diagram of a conjugate generative adversarialnetwork according to an embodiment of the present application.

FIG. 3 is a schematic diagram of a generator according to an embodimentof the present application.

FIG. 4 is a schematic diagram of a discriminator according to anembodiment of the application.

FIG. 5 is a schematic diagram of a medical image noise reduction systemaccording to an embodiment of the present application.

FIG. 6 is a schematic diagram of a terminal according to an embodimentof the present application.

FIG. 7 is a schematic diagram of a storage medium according to anembodiment of the present application,

DETAILED DESCRIPTION

In order to make the purpose, technical solutions and advantages of thepresent application more clearly understood, the present applicationwill be described in further detail below with reference to theaccompanying drawings and embodiments. It should be understood that thespecific embodiments described herein are merely used to explain thepresent application, rather than limit the present application.

FIG. 1 is a flowchart of a medical image noise reduction methodaccording to an embodiment of the present application. The medical imagenoise reduction method according to the embodiment of the presentapplication includes the following operations:

S1: obtaining a standard-dose PET image and a constant-value image (animage whose pixel values are all a constant);

S2: inputting the standard-dose PET image and the constant-value imageinto a decay function to obtain the respective low-dose noisy PET imageand noisy constant-value image;

S3: assembling the low-dose noisy PET image and the noisy constant-valueimage in the width dimension or the height dimension, and then inputtinginto a trained conjugate generative adversarial network, and outputtingthe denoised PET image and constant-value image output through theconjugate generative adversarial

In this operation, the low-dose noisy PET image and the noisyconstant-value image are together input into the generator forprocessing. When the corresponding constant-value image is generatedfrom the noisy constant-value image, the low-dose noisy PET image isalso converted into a denoised PET image. This process is called“conjugation”, and the generative adversarial network constructed basedon this principle is called “conjugate generative adversarial network”.

In the embodiment of this application, the structure of the conjugategenerative adversarial network is shown in FIG. 2 , which includes agenerator G (Generator) and a discriminator D (Discriminator). Thespecific structure of the generator is shown in FIG. 3 , which includesa reflective padding Layer “ReflectionPad(3,3,3,3)”, a convolutionallayer “i1o32k7s1p0”, an instance normalization layer “InstanceNorm”, anonlinear layer “Relu”, a residual module “ResnetBlock”, an upsamplinglayer “Ui512o256k3s2p1”, and a nonlinear layer “Tanh”. The number ofinput channels of the convolution layer is 1, the number of outputchannels is 32, the size of the convolution kernel is 7*7, the step sizeis 1, and the padding is 0; The number of input channels of theupsampling layer is 512, the number of output channels is 256. the sizeof the convolution kernel is 3*3, the step size is 2, and the padding is1, that is, the embodiment of the present application uses deconvolutionto complete the upsampling operation.

The generator includes two parts: feature extraction and imagereconstruction. In the feature extraction part, firstly, the inputlow-dose noisy PET image and noisy constant-value image are processedusing the padding layer, convolution layer, instance normalization layerand nonlinear layer. Secondly, four groups of feature extraction modulesare used to extract features in sequence. The feature extraction moduleincludes a convolution layer, an instance normalization layer and anonlinear layer, where the convolution layer step size of each featureextraction module is 2; with the gradual increase of the extractionmodules, the size of each side of the output feature map of theconvolutional layer becomes half of that of the previous featureextraction module, and the number of the feature maps is twice that ofthe previous feature extraction module. Then, the extracted features areprocessed using 3 residual modules.

In the image reconstruction part, firstly, the PET image and theconstant-value image are gradually reconstructed according to theextracted features through four upsampling modules; then thereconstructed PET image the constant-value image are processed using thepadding layer, the convolution layer and the nonlinear layer, and outputdenoised PET image and constant-value image,

The discriminator D is a convolutional neural network classifier, thestructure of the discriminator is shown in FIG. 4 , which includes aconvolutional layer, an instance normalization layer, and a nonlinearlayer, First, the PET image and the constant-value image generated bythe generator are assembled with the low-dose noisy PET image and thenoisy constant-value image in the channel dimension respectively, andthen input into the discriminator; then, the discriminator performsthree sets of convolution, instance normalization and nonlinearoperations, and finally a convolution layer is used to output theclassification results of the PET image and the constant-value image.

In the embodiment of this application, the Adam optimizer is used totrain the conjugate generative adversarial network. During networktraining, the discriminator and the generator are trained in turn, thatis, each time the discriminator is trained, the generator is trainedthereafter. Therefore, the loss function of the conjugate generativeadversarial network includes the first loss function used when trainingthe discriminator and the second loss function used when training thegenerator. The first loss function used when training the discriminatoris expressed as following using the mean squared error:

L _(D)=

_(β˜P) _(β) [(D(β, α)−b)²]+

_(α˜P) _(α) [(D(G(α), α)−α)²]  (1)

in the above formula, D represents the discriminator network, Grepresents the generator network,

represents the expectation, a represents the input low-dose noisy PETimage, β represents the real standard-dose PET image, α represents 0,and b represents 1;

The second loss function used in the final training generator is isexpressed as the following using one-norm loss function (L1) and meansquare error loss function:

L _(l1)=

_(α˜P) _(α) ∥G(α)−γ∥₁   (2)

L _(gan)=

_(α˜P) _(α) [(D(G(α), α)−b)²]  (3)

In the above formulas (2) and (3), ∥*∥₁ represents one norm, and γrepresents the image after assembling the real standard-dose PET image βand the constant-value image.

In order to further improve the quality of the image generated by thegenerator, the embodiment of the present application further introducesa feature matching loss function, which can be expressed as follows:

$\begin{matrix}{L_{feat} = {{\mathbb{E}}_{({\alpha,\beta})}{\sum_{i = 1}^{T}{\frac{1}{N_{i}}{{{D^{(i)}\left( {\beta,\alpha} \right)} - {D^{(i)}\left( {{G(\alpha)},\alpha} \right)}}}_{1}}}}} & (4)\end{matrix}$

In the above formula (4), D¹ represents the i layer of thediscriminator, N_(i) represents the number of elements in each layer,and T represents the total number of layers of the discriminator.

Then, the second loss function used in the final training generator is:

L _(G) =L _(gan)+λ₁ L _(l1)+λ₂ L _(feat)   (5)

in formula (5), λ₁ and λ₂ represent the weights set by the norm lossfunction L_(l1) and the feature matching loss function L_(feat),respectively.

Based on the above solutions, the medical image noise reduction methodof the present application perform medical image noise reduction byconstructing a conjugate generative adversarial network, and the networkstructure adopts the conjugation mechanism of image conversion, whichstrengthens the constraints on the generative adversarial network, whichcan strengthen the supervision of model training, highlight the trainingobjectives of the model, speed up the convergence speed of the model,and enable the model to learn more essential features, enhance thegeneralization performance of the model, and improve the stability ofthe model, making the training of the medical image noise reductionmodel easier. While improving the peak signal-to-noise ratio andstructural similarity of the image, the image processing capability isenhanced, the quality of low-dose PET imaging is improved, the varianceis reduced, and a more stable and reliable noise reduction effect isobtained. Furthermore, the one-norm loss function and feature matchingloss function of the generated image and the real image are added, whicheffectively improves the quality of the generated image and can bettersupervise the network to approximate the real image.

FIG. 5 is a schematic diagram of a medical image noise reduction systemaccording to an embodiment of the present application. The medical imagenoise reduction system 40 according to the embodiment of the presentapplication includes:

an original image acquisition module 41 used to obtain a standard-dosePET image and a constant-value image (an image whose pixel values areall a constant);

an image attenuation module 42 used to input the standard-dose PET imageand the constant-value image into a decay function to obtain therespective low-dose noisy PET image and noisy constant-value image; and

an image denoising module 43 used to assemble the low-dose noisy PETimage and the noisy constant-value image in the width dimension or theheight dimension, and then inputting into a conjugate generativeadversarial network, and outputting the denoised PET image andconstant-value image output through the conjugate generative adversarialnetwork.

FIG. 6 is a schematic diagram of a terminal according to an embodimentof the present application. The terminal 50 includes a processor 51 anda memory 52 coupled to the processor 51.

The memory 52 stores program instructions for implementing the: medicalimage noise reduction methods described above.

The processor 51 is configured to execute program instructions stored inthe memory 52 to control medical image noise reduction.

The processor 51 may also be referred to as a CPU (Central ProcessingUnit). The processor 51 may be an integrated circuit chip having signalprocessing capability. The processor 51 may also be a general purposeprocessor, digital signal processor (DSP), application specificintegrated circuit (ASIC), off-the-shelf programmable gate array (FPGA)or other programmable logic devices, discrete gates or transistor logicdevices, discrete hardware components. A general purpose processor maybe a microprocessor or the processor may be any conventional processoror the like.

FIG. 7 is a schematic diagram of a storage medium according to anembodiment of the present application. The storage medium of thisembodiment of the present application stores a program file 61 capableof implementing all the above methods, wherein the program file 61 maybe stored in the above-mentioned storage medium in the form of asoftware product, and includes several instructions to make a computerdevice (which can be a personal computer, a server, or a network device,etc.) or a processor to execute all or some of the operations of themethods according to the various embodiments of the present disclosures.The aforementioned storage medium includes: U disk, mobile hard disk,Read-Only Memory (ROM), Random Access Memory (RAM), magnetic disk oroptical disk and other media that can store program codes, or terminaldevices such as computers, servers, mobile phones, and tablets.

The above description of the disclosed embodiments enables any personskilled in the art to make or use the present disclosure. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the generic principles defined in thisdisclosure may be implemented in other embodiments without departingfrom the spirit or scope of this disclosure. Thus, the presentdisclosure is not intended to be limited to the embodiments of thepresent disclosure shown, but is to be accorded the widest scopeconsistent with the principles and novel features disclosed herein.

What is claimed is:
 1. A medical image noise reduction method,comprising: obtaining a standard-dose PET image and a constant-valueimage; inputting the standard-dose PET image and the constant-valueimage into a decay function to obtain a respective low-dose noisy PETimage and noisy constant-value image; assembling the low-dose noisy PETimage and the noisy constant-value image in a width dimension or aheight dimension, and then inputting into a trained conjugate generativeadversarial network, and outputting a denoised PET image andconstant-value image output through the conjugate generative adversarialnetwork.
 2. The medical image noise reduction method of claim 1, whereinthe conjugate generative adversarial network comprises a generator and adiscriminator; wherein the generator comprises a reflective paddinglayer, a convolution layer, an instance normalization layer, a nonlinearlayer, a residual module, an upsampling layer, and a nonlinear layer;and wherein the discriminator is a convolutional neural networkclassifier, and comprises a convolutional layer, an instancenormalization layer, and a nonlinear layer.
 3. The medical image noisereduction method of claim 2, wherein the generator comprises two parts:feature extraction and image reconstruction; wherein in the featureextraction part, the input low-dose noisy PET image and noisyconstant-value image are first processed using the padding layer, theconvolutional layer, the instance normalization layer and the nonlinearlayer; then four groups of feature extraction modules are used toperform feature extraction on the low-dose noisy PET image and the noisyconstant-value image; then the extracted features are processed usingthree residual modules; wherein in the image reconstruction part, thePET image and the constant-value image are first gradually reconstructedthrough four upsampling modules based on the extracted features; thenthe reconstructed PET image and constant-value image are processed usingthe padding layer, the convolution layer and the nonlinear layer, andthe denoised PET image and constant-value image are output.
 4. Themedical image noise reduction method of claim 3, wherein the featureextraction module comprises a convolution layer, an instancenormalization layer, and a nonlinear layer, wherein a convolution layerstep size of each feature extraction module is 2; with the gradualincrease of the extraction modules, a size of each side of an outputfeature map of the convolutional layer becomes half of that of theprevious feature extraction module, and a number of the feature maps istwice that of the previous feature extraction module.
 5. The medicalimage noise reduction method of claim 3, wherein the PET image and theconstant-value image generated by the generator are assembled with thelow-dose noisy PET image and the noisy constant-value image in a channeldimension respectively, and then input into the discriminator; then thediscriminator performs three sets of convolution, instancenormalization, and nonlinear operations, and finally a convolution layeris used to output a classification result of the PET image and theconstant-value image generated by the generator.
 6. The medical imagenoise reduction method of claim 2, wherein a loss function of theconjugate generative adversarial network comprises a first loss functionused when training the discriminator and a second loss function usedwhen training the generator, wherein the first loss function isrepresented by mean square error as:L _(D)=

_(β˜P) _(β) [(D(β, α)−b)²]+

_(α˜P) _(α) [(D(G(α), α)−α)²] where in the above formula, D represents adiscriminator network, represents a generator network,

represents an expectation, α represents the input low-dose noisy PETimage, β represents a real standard-dose PET image, a represents 0, andb represents 1; wherein the second loss function is expressed as thefollowing using one-norm loss function and mean square error lossfunction:L _(l1)=

_(α˜P) _(α) ∥G(α)−γ∥₁L _(gan)=

_(α˜P) _(α) [(D(G(α), α)−b)²] where in the above formula, ∥*∥ representsone norm, and γ represents the image after assembling the realstandard-dose PET image β and the constant-value image.
 7. The medicalimage noise reduction method of claim 3, wherein a loss function of theconjugate generative adversarial network comprises a first loss functionused when training the discriminator and a second loss function usedwhen training the generator, wherein the first loss function isrepresented by mean square error as:L _(D)=

_(β˜P) _(β) [(D(β, α)−b)²]+

_(α˜P) _(α) [(D(G(α),−(α)²] where in the above formula, D represents adiscriminator network, represents a generator network,

represents an expectation, a represents the input low-dose noisy PETimage, β represents a real standard-dose PET image, α represents 0, andb represents 1; wherein the second loss function is expressed as thefollowing using one-norm loss function and mean square error lossfunction:L _(l1)=

_(α˜P) _(α) ∥G(α)−γ∥₁L _(gan)=

_(α˜P) _(α[() D(G(α), α)−b)²] where in the above formula, ∥*∥₁represents one norm, and γ represents the image after assembling thereal standard-dose PET image β and the constant-value image.
 8. Themedical image noise reduction method of claim 4, wherein a loss functionof the conjugate generative adversarial network comprises a first lossfunction used when training the discriminator and a second loss functionused when training the generator, wherein the first loss function isrepresented by mean square error as:L _(D)=

_(β˜P) _(β) [(D(β, α)−b)²]+

_(α˜P) _(α) [(D(G(α), α−)²] where in the above formula, D represents adiscriminator network, G represents a generator network,

represents an expectation, α represents the input low-dose noisy PETimage, β represents a real standard-dose PET image, α represents 0, andb represents 1; wherein the second loss function is expressed as thefollowing using one-norm loss function and mean square error lossfunction:L _(l1)=

_(α˜P) _(α) ∥G(α)−γ∥₁L _(gan)=

_(α˜P) _(α) [(D(G(α), α)−b ²] where in the above formula, ∥*∥ representsone norm, and γ represents the image after assembling the realstandard-dose PET image β and the constant-value image.
 9. The medicalimage noise reduction method of claim 5, wherein a loss function of theconjugate generative adversarial network comprises a first loss functionused when training the discriminator and a second loss function usedwhen training the generator, wherein the first loss function isrepresented by mean square error as:L _(D)=

_(β˜P) _(β) [(D(β, α)−b)²]+

_(α˜P) _(α) [(D(G(α), α)−α)²] where in the above formula, D represents adiscriminator network, G represents a generator network,

represents an expectation, α represents the input low-dose noisy PETimage, β represents a real standard-dose PET image, α represents 0, andb represents 1; wherein the second loss function is expressed as thefollowing using one-norm loss function and mean square error lossfunction:L _(l1)=

_(α˜P) _(α) ∥G(α)−γ∥₁L _(gan)

_(α˜P) _(α[() D(G(α), α)−b)²] where in the above formula, ∥*∥₁represents one norm, and γ represents the image after assembling thereal standard-dose PET image β and the constant-value image.
 10. Themedical image noise reduction method of claim 6, wherein the lossfunction of the conjugate generative adversarial network furthercomprises a feature matching loss function:$L_{feat} = {{\mathbb{E}}_{({\alpha,\beta})}{\sum\limits_{i = 1}^{T}{\frac{1}{N_{i}}{{{D^{(i)}\left( {\beta,\alpha} \right)} - {D^{(i)}\left( {{G(\alpha)},\alpha} \right)}}}_{1}}}}$where in the above formula, D^(i) represents an ith layer of thediscriminator, N_(i) represents a number of elements in each layer, andT represents a total number of layers of the discriminator,
 11. Amedical image noise reduction system, comprising: an original imageacquisition module used to obtain a standard-dose PET image and aconstant-value image; an image attenuation module used to input thestandard-dose PET image and the constant-value image into a decayfunction to obtain the respective low-dose noisy PET image and noisyconstant-value image; an image denoising module used to assemble thelow-dose noisy PET image and the noisy constant-value image in a widthdimension or a height dimension, and then inputting into a trainedconjugate generative adversarial network, and output a denoised PETimage and constant-value image output through the conjugate generativeadversarial network.
 12. A terminal, comprising a processor and anon-transitory computer-readable memory that is coupled to theprocessor, wherein the non-transitory computer-readable memory isconfigured to store program instructions for implementing the medicalimage noise reduction method of claim 1; the processor is configured toexecute the program instructions stored in the non-transitorycomputer-readable memory to perform medical image noise reduction. 13.The terminal of claim 12, wherein the conjugate generative adversarialnetwork comprises a generator and a discriminator; wherein the generatorcomprises a reflective padding layer, a convolution layer, an instancenormalization layer, a nonlinear layer, a residual module, an upsamplinglayer, and a nonlinear layer; and wherein the discriminator is aconvolutional neural network classifier, and comprises a convolutionallayer, an instance normalization layer, and a nonlinear layer.
 14. Theterminal of claim 13, wherein the generator comprises two parts: featureextraction and image reconstruction; wherein in the feature extractionpart, the input low-dose noisy PET image and noisy constant-value imageare first processed using the padding layer, the convolutional layer,the instance normalization layer and the nonlinear layer; then fourgroups of feature extraction modules are used to perform featureextraction on the low-dose noisy PET image and the noisy constant-valueimage; then the extracted features are processed using three residualmodules; wherein the image reconstruction part, the PET image and theconstant-value image are first gradually reconstructed through fourupsampling modules based on the extracted features; then thereconstructed PET image and constant-value image are processed using thepadding layer, the convolution layer and the nonlinear layer, and thedenoised PET image and constant-value image are output.
 15. The terminalof claim 14, wherein the feature extraction module comprises aconvolution layer, an instance normalization layer, and a nonlinearlayer, wherein a convolution layer step size of each feature extractionmodule is 2; with the gradual increase of the extraction modules, a sizeof each side of an output feature map of the convolutional layer becomeshalf of that of the previous feature extraction module, and a number ofthe feature maps is twice that of the previous feature extractionmodule.
 16. The terminal of claim 14, wherein the PET image and theconstant-value image generated by the generator are assembled with thelow-dose noisy PET image and the noisy constant-value image in a channeldimension respectively, and then input into the discriminator; then thediscriminator performs three sets of convolution, instancenormalization, and nonlinear operations, and finally a convolution layeris used to output a classification result of the PET image and theconstant-value image generated by the generator.
 17. The terminal ofclaim 13, wherein a loss function of the conjugate generativeadversarial network comprises a first loss function used when trainingthe discriminator and a second loss function used when training thegenerator, wherein the first loss function is represented by mean squareerror as:L _(D)=

_(β˜P) _(β) [(D(β, α)−b)²]+

_(α˜P) _(α) [(D(G(α), α)−α)²] where in the above formula, D represents adiscriminator network, G represents a generator network,

represents an expectation, a represents the input low-dose noisy PETimage, represents a real standard-dose PET image, a represents 0, and βrepresents 1; wherein the second loss function is expressed as thefollowing using one-norm loss function and mean square error lossfunction:L _(l1)=

_(α˜P) _(α) ∥G(α)−γ∥₁L _(gan)=

_(α˜P) _(α) [(D(G(α), α)−b)²] where in the above formula, ∥*∥₁represents one norm, and γ represents the image after assembling thereal standard-dose PET image β and the constant-value image.
 18. Anon-transitory computer-readable storage medium, storing programinstructions executable by a processor, wherein the program instructionsare used to execute the medical image noise reduction method of claim 1.19. The non-transitory computer-readable storage medium of claim 18,wherein the conjugate generative adversarial network comprises agenerator and a discriminator: wherein the generator comprises areflective padding layer, a convolution layer, an instance normalizationlayer, a nonlinear layer, a residual module, an upsampling layer, and anonlinear layer; and wherein the discriminator is a convolutional neuralnetwork classifier, and comprises a convolutional layer, an instancenormalization layer, and a nonlinear layer,
 20. The non-transitorycomputer-readable storage medium of claim 19, wherein the generatorcomprises two parts: feature extraction and image reconstruction;wherein in the feature extraction part, the input low-dose noisy PETimage and noisy constant-value image are first processed using thepadding layer, the convolutional layer, the instance normalization layerand the nonlinear layer; then four groups of feature extraction modulesare used to perform feature extraction on the low-dose noisy PET imageand the noisy constant-value image; then the extracted features areprocessed using three residual modules; wherein in the imagereconstruction part, the PET image and the constant-value image arefirst gradually reconstructed through four upsampling modules based onthe extracted features; then the reconstructed PET image andconstant-value image are processed using the padding layer, theconvolution layer and the nonlinear layer, and the denoised PET imageand constant-value image are output.